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3D Object Detection for Autonomous Driving: A Practical Survey

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dc.affiliation.dptoUC3M. Departamento de Ingeniería de Sistemas y Automática
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Sistemas Inteligentes
dc.contributor.authorRamajo, Álvaro
dc.contributor.authorEscalera Hueso, Arturo de la
dc.contributor.authorArmingol Moreno, José María
dc.contributor.funderComunidad de Madrid
dc.contributor.funderMinisterio de Ciencia e Innovación (España)
dc.date.accessioned2024-06-17T09:28:26Z
dc.date.available2024-06-17T09:28:26Z
dc.date.issued2023-04-26
dc.descriptionProceeding of: 9th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2023), Prague (Czech Republic ), 26th to 28th May 2023.
dc.description.abstractAutonomous driving has been one of the most promising research lines in the last decade. Although still far off the sought-after level 5, the research community shows great advancements in one of the most challenging tasks: the 3d perception. The rapid progress of related fields like Deep Learning is one the reasons behind this success. This enables and improves the processing algorithms for the input data provided by LiDAR, cameras, radars and such other devices used for environment perception. With such growing knowledge, reviewing and structuring the state-of-the-art solutions becomes a necessity in order to correctly address future research directions. This paper provides a comprehensive survey of the progress of 3D object detection in terms of sensor data, available datasets, top-performing architectures and most notable frameworks that serve as a baseline for current and upcoming works.
dc.description.sponsorshipGrant PID2019-104793RB-C31 and PDC2021121517-C31 funded by MCIN/AEI/10.13039/50110 0011033 and by the European Union “NextGenerationEU/PRTR” and the Comunidad de Madrid through SEGVAUTO-4.0-CM (P2018/EMT-4362).
dc.format.extent10
dc.identifier.bibliographicCitationRamajo Ballester, A., Escalera Hueso, A. de la, Armingol Moreno, J. M. (2023). 3D Object Detection for Autonomous Driving: A Practical Survey. Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2023). Portugal: Science and Technology Publications, pp. 64-73
dc.identifier.doi10.5220/0011748400003479
dc.identifier.isbn978-989-758-652-1
dc.identifier.publicationfirstpage64
dc.identifier.publicationlastpage73
dc.identifier.publicationtitleProceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2023)en
dc.identifier.urihttps://hdl.handle.net/10016/43983
dc.identifier.uxxiCC/0000034972
dc.language.isoeng
dc.publisherSCITEPRESSen
dc.relation.eventdate26-28 April 2023
dc.relation.eventplacePraga (República Checa)es
dc.relation.eventtitle9th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2023en
dc.relation.projectIDComunidad de Madrid. S2018/EMT-4362
dc.relation.projectIDGobierno de España. PID2019-104793RB-C31
dc.relation.projectIDGobierno de España. PDC2021-121517-C31
dc.rights© 2023 by SCITEPRESS–Science and Technology Publications, Lda.
dc.rightsUnder CC license (CC BY-NC-ND 4.0)
dc.rights.accessRightsopen access
dc.subject.ecienciaRobótica e Informática Industriales
dc.subject.other3d Object Detectionen
dc.subject.otherAutonomous Drivingen
dc.subject.otherDeep Learningen
dc.title3D Object Detection for Autonomous Driving: A Practical Surveyen
dc.typeconference proceedingsen
dc.type.hasVersionVoR
dspace.entity.typePublication
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